The present invention relates generally to the field of computer vision, and more particularly to visual object detection within images.
Computer vision—the field of using computers for vision perception purposes—is known. Computer vision includes methods for acquiring, processing, analyzing, and understanding images in order to produce numerical or symbolic information. Visual object detection (or “object detection”) is a known sub-domain of computer vision that involves finding and identifying objects in digital images and/or videos (for a further definition of “object detection,” see the Definitions sub-section of the Detailed Description section, below).
In the field of computer vision, an approach called Non-Maximum Suppression (NMS) is employed. Generally speaking, NMS is an edge thinning technique used to remove unwanted data on the edge of an image. NMS is commonly used in object detection to eliminate repeated detections of an object.
Another approach to optimizing computer vision is the use of integral images (also sometimes referred to as “summed area tables”). Generally speaking, an integral image computes, for each pixel in an image, a value equal to the sum of all pixel values above and to the left of the respective pixel, including the pixel itself. Because an integral image is determined from values above and to the left of each pixel, integral images can be computed in a single pass-through of an image.